System, method, and computer program for forecasting residual values of a durable good over time

ABSTRACT

Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of and claims a benefit of priorityunder 35 U.S.C. 120 of the filing date of U.S. patent application Ser.No. 13/967,148, filed Aug. 14, 2013, entitled “SYSTEM, METHOD ANDCOMPUTER PROGRAM FOR FORECASTING RESIDUAL VALUES OF A DURABLE GOOD OVERTIME,” issued as U.S. Pat. No. 9,607,310, which claims a benefit ofpriority from U.S. Provisional Application No. 61/683,552, filed Aug.15, 2012, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FOR FORECASTINGRESIDUAL VALUES OF A DURABLE GOOD OVER TIME,” which are herebyincorporated by reference as if set forth herein in their entirety.

TECHNICAL FIELD

This disclosure relates generally to forecasting future market value ofdurable goods, and more particularly to systems, methods and computerprogram products for forecasting the value of an item usingmicroeconomic, macroeconomic, and competitive set information andupdating the forecast value at predetermined time intervals.

BACKGROUND OF THE RELATED ART

The market value of an item is known at the time that it is sold to aconsumer. After this initial transaction, however, the value of the itemwill decline. The amount by which the value decreases may depend uponmany factors, such as the amount of time that has passed since theoriginal sale, the amount of wear experienced by the item, and so on.

Because of the difficulty of determining these factors with anycertainty, the value of an item after its initial sale is conventionallydetermined by resale values of the item. For instance, the value of atwo-year-old automobile is determined by examining the prices for whichsimilarly equipped automobiles of the same make, model and year haveactually sold. While some adjustments may be made to these values (e.g.,for vehicle mileage above or below some average range), determination ofthe automobile's value generally relies on past resale prices of thesame vehicle.

Since these conventional methods of determining the value of an item arerelatively simplistic and take into account only backward-looking data(e.g., past sales of the item), they are not as accurate as may bedesired. For instance, an automobile leasing company may need to knowthe future value of the automobiles that it owns in order to obtainfinancing for expansion or other business transactions. It wouldtherefore be desirable to provide improved methods for determining thefuture value of such items.

SUMMARY OF THE DISCLOSURE

This disclosure is directed to systems, methods and computer programproducts for forecasting future values of an item that solve one or moreof the problems discussed above. One particular embodiment is a systemfor forecasting future values of an item, where the system includes aserver computer coupled to a network and a local data storage devicecoupled to the server computer. The server computer is configured toreceive data from one or more data sources external to the system. Theserver computer determines an initial value for an item, and thendetermines forecasted future values for the item which are adjustedbased on microeconomic data which is specific to an industry of the itemand macroeconomic data which is non-specific to the industry of theitem. The server computer stores the forecasted future values in thelocal data storage device and enables access by a user to the forecastedfuture values. The server computer can modify the forecasted futurevalues based on editorial input provided by the user and store themodified values in the local data storage device. The server computercan enable access by a client device to the modified forecasted futurevalues through the network.

The server computer may be configured to receive and store competitiveset data, and to modify the one or more future values based on thecompetitive set data. The server computer may have one or more crawlersthat are configured to query the data sources via the network and toobtain data responsive to the queries. The server computer may beconfigured to scrub the data received from the data sources, and mayidentify and remove erroneous data. The server computer may beconfigured to determine one or more update intervals, to update thefuture values at the determined intervals, and to store the updatedfuture values. The system may include a workbench that provides a userinterface through which a user can access the future values, where theserver modifies the future values based on input provided by the userthrough the workbench.

An alternative embodiment comprises a method for forecasting futurevalues of an item. The method includes receiving data from one or moredata sources via a network and determining an initial value for an itembased on the received data. One or more future values are thendetermined for the item, where the future values are adjusted based onmicroeconomic data which is specific to the item's industry andmacroeconomic data which is non-specific to the industry. The futurevalues are then stored in a data storage device, and access to thefuture values by a user is enabled. The future values may be modifiedbased on editorial input provided by a user. The modified future valuesmay be stored in the data storage device, and access by a client deviceto the modified future values may be enabled.

Another alternative embodiment comprises a computer program productincluding a non-transitory computer-readable storage medium that storescomputer instructions. The instructions are translatable by a processorto perform a method. The method may include receiving data from one ormore data sources via a network and determining an initial value for anitem based on the received data. One or more future values are thendetermined for the item, where the future values are adjusted based onmicroeconomic data which is specific to the item's industry andmacroeconomic data which is non-specific to the industry. The futurevalues are then stored in a data storage device, and access to thefuture values by a user is enabled. The future values may be modifiedbased on editorial input provided by a user. The modified future valuesmay be stored in the data storage device, and access by a client deviceto the modified future values may be enabled.

Numerous other embodiments are also possible.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention may become apparent uponreading the following detailed description and upon reference to theaccompanying drawings.

FIG. 1 is a diagram illustrating the structure of an exemplary system inaccordance with one embodiment.

FIG. 2 is a diagram illustrating the collection of different types ofdata that are collected by the system of one embodiment.

FIG. 3 is a diagram illustrating the collection of data from differentdata sources in the system of one embodiment.

FIG. 4 is a flow diagram illustrating the acquisition of data used inthe generation of a forecast for an item in one embodiment.

FIG. 5 is a flow diagram illustrating the generation of a baselineforecast in one embodiment.

FIG. 6 is a flow diagram illustrating the adjustment of a baseline curvein one embodiment.

FIG. 7 is a flow diagram illustrating the adjustment of a residual curveby a client in one embodiment.

FIG. 8 is a diagram illustrating an exemplary baseline forecast orresidual curve.

FIG. 9 is a diagram illustrating an exemplary residual curve adjustmentin one embodiment.

While the invention is subject to various modifications and alternativeforms, specific embodiments thereof are shown by way of example in thedrawings and the accompanying detailed description. It should beunderstood, however, that the drawings and detailed description are notintended to limit the invention to the particular embodiment which isdescribed. This disclosure is instead intended to cover allmodifications, equivalents and alternatives falling within the scope ofthe present invention as defined by the appended claims. Further, thedrawings may not be to scale, and may exaggerate one or more componentsin order to facilitate an understanding of the various featuresdescribed herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

One or more embodiments of the invention are described below. It shouldbe noted that these and any other embodiments described below areexemplary and are intended to be illustrative of the invention ratherthan limiting.

For the purposes of this disclosure, the term “item” may be used torefer to a durable good, product, or any item that has a known value atthe time it was first sold and that has a different resale value overtime thereafter. Examples of an item may include a vehicle, a realestate property, etc.

For a durable good or product (an “item”), the resale value of the itemmay be affected by various factors such as time, the availability ofsame or similar items, the geographical location where the itemphysically resides, demand in for the item in the resale market and/orindustry, the purchasing power of the target buyers, and so on. Anability to determine the amount by which the item will change (e.g.,devalue) over time, and thereby forecast the resale or residual value ofthe item can provide a better understanding of a company's assets andcan allow the company to make better business decisions.

Embodiments disclosed herein provide a methodology for forecastingresidual values of an item in two time periods and determining changesin value in a valuation metric. By estimating the changes in value forsuccessive future time intervals, a function can be constructed tocapture the estimated relationship between time and the item's value.Implementing the methodology, embodiments provide a model which canpredict the residual value of an item at a future time point for anytime period. In one embodiment, the current market value of an item atthe beginning of an estimation period is known and can be used as abaseline against which future values are computed. The farther away intime a forecast is relative to the baseline, the more uncertainty willexist. Thus, the forecasting error will grow as the width of the timeinterval increases.

Taking this uncertainty into consideration, embodiments utilizedifferent types of variables to aid in forecasting residual values of anitem over time. Example types of forecasting variables include, but arenot limited to, modifications to the items, locality of the items,depreciation of the items, microeconomic factors, macroeconomic factors,and sets of competitive items. These factors will be discussed in moredetail below.

Referring to FIG. 1, a diagram illustrating the structure of anexemplary system in accordance with one embodiment is shown. FIG. 1 is asimplified diagrammatic representation of a network architecture 100 inwhich this embodiment is implemented. For purposes of clarity, a singleclient computer, a single server computer, and a single data source areshown in the figure. The client and server computers and data sourcerepresent an exemplary hardware configuration of data processing systemsthat are capable of bi-directionally communicating with each other overa public network such as the Internet. Those skilled in the art willappreciate that enterprise computing environment 130 may comprisemultiple server computers, and multiple client computers and datasources may be bi-directionally coupled to enterprise computingenvironment 130 over network 120.

Client computer 110 can include central processing unit (“CPU”) 111,read-only memory (“ROM”) 113, random access memory (“RAM”) 115, harddrive (“HD”) or storage memory 117, and input/output device(s) (“I/O”)119. I/O 119 can include a keyboard, monitor, printer, and/or electronicpointing device. Example of I/O 119 may include mouse, trackball,stylus, or the like. Client computer 110 can include a desktop computer,a laptop computer, a personal digital assistant, a cellular phone, ornearly any device capable of communicating over a network. Servercomputer 140 may have similar hardware components including CPU 141, ROM143, RAM 145, HD 147, and I/O 149. Data source 160 may be a servercomputer having hardware components similar to those of client computer110 and server computer 130, or it may be a data storage device.

Each computer shown in FIG. 1 is an example of a data processing system.ROM 113 and 143, RAM 115 and 145, HD 117 and 147, and database 150 caninclude media that can be read by CPU 111 and/or 141. Therefore, thesetypes of computer memories include computer-readable storage media.These memories may be internal or external to computers 110 and/or 140.

Portions of the methods described herein may be implemented in suitablesoftware code that may reside within ROM 143, RAM 145, HD 147, database150, or a combination thereof. In some embodiments, computerinstructions implementing an embodiment disclosed herein may be storedon a direct access storage device (DASD) array, magnetic tape, floppydiskette, optical storage device, or other appropriate computer-readablestorage medium or storage device. A computer program productimplementing an embodiment disclosed herein may therefore comprise oneor more computer-readable storage media storing computer instructionstranslatable by CPU 141 to perform an embodiment of a method disclosedherein.

In an illustrative embodiment, the computer instructions may be lines ofcompiled C++, Java, or other language code. Other architectures may beused. For example, the functions of server computer 140 may bedistributed and performed by multiple computers in enterprise computingenvironment 130. Accordingly, each of the computer-readable storagemedia storing computer instructions implementing an embodiment disclosedherein may reside on or accessible by one or more computers inenterprise computing environment 130.

As noted above, the present systems and methods utilize not onlydepreciation and other conventionally employed information to determinethe future value of an item, but also microeconomic information,macroeconomic information and information on sets of competitive items.Referring to FIGS. 2 and 3, a pair of diagrams illustrating thecollection of this information is shown. FIG. 2 illustrates thedifferent types of data that are collected by the present system, whileFIG. 3 illustrates the different sources from which the data istypically collected.

As depicted in FIG. 2, server 140 collects data types 161-166. Datatypes 161-163 represent information that is collected by the server.Data types 161-163 represent information that is related to past salesof similar items. The past sales indicate the amount of depreciationthat the similar items have experienced from their original value toresale, and also indicate the effects of modifications and locality onresale value. Data sources 164-166, on the other hand, represent typesof information that are not conventionally collected for use indetermining the future value of an item. These data sources representinformation that indicates how the value of the item will be impacted inthe future, rather than how it was affected in the past.

Modifications 161 reflect any changes to an item i that may affect itsvalue at any time point. Examples of modifications (M_(i)) includeoptions added to the item in prior periods, differentconfigurations/styles of the item, or other features which maydistinguish one item from another that is produced by the samemanufacturer.

Locality 162 represents valuation differences in an industry (p) thatvary geographically. Examples of Locality (L_(p)) would includeadjustments to equalize sales of the essentially identical items made indifferent locations, allowing valuation to be conducted, for instance,at both the national and state/province levels.

Depreciation 163 represents the natural change in value that occurs asthe item i is used over time. Depreciation (D_(i)) can be determinedfrom past sales of the item.

Microeconomic information 164 is specific to the industry p of the itemi (i∈p). For example, microeconomic factors (G_(p)) may include supplyand/or demand in the industry, industry trends, seasonality and/orvolatility of the item, or information about a company that is in theindustry.

Macroeconomic information 165 is non-specific to the item and itsindustry. Macroeconomic factors (F) may relate to the overall economy,rather than to the specific industry with which the item i is associated(e.g., the real estate or automotive industries). Examples ofmacroeconomic information may include inflation, unemployment, andinterest rates.

Competitive set information 166 relates to a set of items that competewith the item of interest. Competitive sets (C_(iU)) include all otheritems, j=1, . . . , J (i≠j), in the same industry p and in thecompetitive set U (i, j∈U∀j) which are reasonable substitutes for theitem i being valued. Examples of competitive items j may include itemsproduced by different manufacturers that share similarities with theitem i being valued and also sales incentives applied to the item ibeing valued or its substitutes. Competitive set information may includesuch information as sales or recall information for competing items.

Referring to FIG. 3, typical sources for data types 161-166 are shown.Depreciation, modification and locality data types are commonly obtainedfrom data sources that are associated with auctions or wholesalers (167)dealing in the item of interest. Microeconomic information and data oncompetitive sets associated with the item of interest are commonlyobtained from industry databases (168). Macroeconomic information may beobtained from databases or other sources associated with nationaleconomics (169).

Referring to FIGS. 4-7, a set of flow diagrams is shown. The diagramsillustrate a method for forecasting the future value of an item andenabling access by a client to the forecast in accordance with oneembodiment. The method may be implemented, for example, in a system suchas the one shown in FIG. 1. It should be noted that the particular stepsillustrated in the figures are exemplary, and the steps of alternativeembodiments may vary from those shown in the figures.

Referring to FIG. 4, a flow diagram illustrating the acquisition of dataused in the generation of a forecast for an item is shown. The serverinitially queries one or more data sources for information (405). Thedata sources may include both data storage units internal to theenterprise computing environment and data sources external to theenterprise computing environment. In one embodiment, the enterprisecomputing environment may have crawlers that query external datasources, searching for information relevant to the forecast. The servercollects the data (410) and stores the data for further processing(415). The data that is collected is examined by the server (420) andprocessed to identify portions of the data that will be used to generatethe forecast. The data may be “scrubbed” by the server (425) in order toprovide a better basis for the forecast. The scrubbing process mayinvolve various techniques to improve the quality of the data, such asidentifying data that appears to be in error, removing outlying datapoints that substantially deviate from the remainder of the data, and soon. The data may also be filtered or examined to identify particularfields or types of data within the data that has been collected by thesystem. Still further, the system may transform all or part of thecollected data into forms that are suitable for use by the system. Afterthe desired data has been selected and scrubbed, if necessary, themodified data set is stored (430) in a local data storage device, fromwhich it can be retrieved and used in the generation of the forecast.

Referring to FIG. 5, a flow diagram illustrating the generation of abaseline forecast is shown. The server first determines an initial valuefor the item of interest (505). This value is commonly determined basedupon past sales of the item. The server then adjusts the value of theitem to account for modifications to the item that may affect its value(510). For instance, if the item has been upgraded in some manner, itsvalue may have increased. The server may also adjust the value toaccount for things such as excessive wear on the item. The server mayfurther adjust the value of the item to account for locality (515). Thelocality of the item may impact its value for various reasons, such asdiffering demand in different areas, different levels of wear in thedifferent locations, etc. Using this initial value of the item, theserver then generates a time-based value for the item which takes intoaccount the depreciation of the item, as well as various microeconomicand macroeconomic factors (520). The microeconomic and macroeconomicfactors effectively increase or decrease the rate of depreciation,depending upon the specific information that is taken into account. Theresulting forecast of the value of the item over time is stored in alocal data storage device (525) as a baseline for distribution orfurther adjustment. An exemplary baseline forecast or residual curve 800is illustrated in FIG. 8.

Referring to FIG. 6, a flow diagram illustrating the adjustment of abaseline curve is shown. After the baseline residual curve has beengenerated and stored in a local data storage device, a user of theenterprise computing environment can provide editorial input (605) thatis used to revise the residual curve (610). The editorial input may beprovided to account for any factors that were not accounted for in thegeneration of the baseline curve, or that have changed since thebaseline curve was generated. The editorial input may also be providedto determine the potential affect of various factors on the residualcurve. The editorial input may be provided through a workbenchapplication that allows the user to see the results of the input. Theresidual curve that is revised according to the editorial input can thenbe made “live” (615). In other words, the revised residual curve can bestored or published to a location to which client access can be enabled.It may be desirable to periodically revise the residual curve. If it istime to do so (620) (e.g., if a predetermined interval has beenreached), the user can provide additional editorial input (605) forgeneration of a newly revised residual curve (610), which can then bepublished for access by the client (615).

In one embodiment, the residual curve is updated at regular intervals.The updated residual curve can be stored in place of the previousbaseline curve and used as the baseline for future use. When theresidual curve is updated, several comparisons are made to ensure thatthe newly revised curve is reasonable. For example, the revised curve iscompared to the previous curve to determine whether the values of thenew curve differ from the previous curve by a substantial amount. If thedifference is too great, this may indicate that the inputs to therevised curve are not realistic. The inputs may therefore be adjusted tobring the revised residual curve closer to the previous curve. In oneembodiment, the residual curve is also adjusted based on the currentvalues of items in a competitive set. For instance, the curve may beadjusted to bring the curve closer to the value of a closest competitiveitem, or to the average value of the set of competitive items. Referringto FIG. 9, an exemplary residual curve adjustment is shown. In thisfigure, the dotted line (900) represents the initial computation of thecurve. Points A, B, and C represent the average residual values of thecompetitive set, the current market value of the best matching item inthe competitive set, and the previous value of the item of interest,respectively. Taking these data points into account, the final revisedresidual curve is shown as line 910.

Referring to FIG. 7, a flow diagram illustrating the adjustment of aresidual curve by a client is shown. After the baseline residual curveis revised by the editorial input, the server can enable access by theclient to the revised curve (705). Customers can access the residualcurve through the client in order to determine the value of the item atsome point in the future. The client in this embodiment includes aworkbench application that allows the customer to vary some of thefactors that affect the residual curve and to view the resulting changesto the residual curve. The server receives input from the client'sworkbench application (710) and revises the residual curve according tothe received input (715). The newly revised residual curve is thenprovided to the client (720) so that it can be viewed by the customer.

In one embodiment, the residual values of an item i (V_(i,n)) may beexpressed in an equation below as a function of time t_(n) (n=0, . . . ,T):V _(i,n)=(V′ _(i,0) +M _(i,n))×(τ_(i,n) ×L _(p,n))×D _(j,n|0)+(F_(.,n|n-h) +G _(k,n|n-h))+C _(iU,n|n*) h=1, . . . , H

In the above equation, “F.” implies that the macroeconomic factors aretaken over all p=1, . . . , P and “n*” pertains to period t_(n)* definedas a reference period, t_(n)*, at which adjustments will be made toalign values with other items in the competitive set. Further,

-   -   V′_(i,0) represents the market value at time t₀ before        modifications, reflecting the level of the base configuration of        item i at period t₀, prior to modifications and locality        adjustments,    -   V_(i,n) reflects the level of the variable for the item i at        period t_(n),    -   L_(p,n) reflects the locality adjustment made at time t_(n) to        all items i_(n) for industry p (i∈P),    -   F_(.,n|n-h) reflects the level of a macroeconomic (neither        industry-nor item-specific) variable at period t_(n), given the        historical information about that the variable in the last=1, .        . . , H periods (t_(n-1), t_(n-2), . . . , t_(n-H)),    -   G_(p,n|n-h) reflects the level of the microeconomic variable at        period t_(n) given the historical information for industry p        (i∈p) available about that variable in the last=1, . . . , H        periods (t_(n-1), t_(n-2), . . . , t_(n-H)),    -   D_(1,n|0) reflects the observed natural depreciation for another        item j in the same competitive set as item I at time period t₀,    -   C_(iU,n|n*) reflects a competitive set adjustment made to item i        based at time period t_(n*) based on an observed discrepancy        between V_(i,n) and the predicted values of all other items,        j=1, . . . , J (i≠j) in the competitive set U (i, j∈U∀j)        evaluated at some reference period, t_(n).

Following is an exemplary methodology that can be used to estimate thevalue of an item over time, thereby providing the resale value of theitem that could be expected at future time points.

In this embodiment, a method for forecasting residual values of an itembegins with determining a baseline value of an item. The baseline valueof the item can be an unmodified value for the item that is obtainedthrough direct observation of the current market values. If the baselinevalue cannot be determined or obtained directly for the item, acompetitive set of similar and substitute items in the same industry asthe item may be constructed. The item in the competitive set that is themost similar to the item of interest (the item being evaluated) can beselected as a substitute and its value can be used as the baseline valuefor the item. This baseline value reflects the market information acrosslocalities in which the item is available.

The method further includes determining, at an initial time point t₀, areference period at which adjustments are made to align the baselinevalue of the item with values of other items in a competitive set ofsimilar and substitute items in the same industry as the item. In oneembodiment, this competitive set can be the same competitive set used inselecting a substitute item whose value is used as the baseline valuefor the item under evaluation. The reference period may be determinedutilizing a set of constraints and/or considerations. Exampleconstraints and considerations may include the expected lifespan for theitem, the purpose for obtaining the residual value of the item, howoften input data to the model (for predicting the residual value of theitem at a future time point for any time period) is updated, etc. As anexample, suppose the initial time point is Jul. 9, 2012 and input datato the model is updated on a monthly basis, the reference period couldthen be Jul. 9, 2012 to Aug. 9, 2012, Jul. 9, 2012 to Sep. 9, 2012, Jul.9, 2012 to Oct. 9, 2012, etc. The reference period can be furtherconstrained by the total expected lifetime of the item. For example, ifthe item is not expected to retain value after five years, then thereference period can be Jul. 9, 2012 to Jul. 9, 2017, or less (in one ormore monthly temporal offsets as constrained by the update frequency ofthe input data to the model).

Once the reference period has been determined, a number of forecastsdesired between the initial time point and the reference period can bedetermined. This time determines how often a forecast of the residualvalue of the item is to be generated. Starting from the initial timepoint, the time interval at which a forecast is to be generated can bethe same as, or more than, the update frequency of the input data to themodel. Following the above example in which the expected lifespan of theitem is five years, if it is assumed that the reference period is twoyears, there can be, for example, 23 forecasts, each of which isgenerated at a fixed time interval of one month. If the time interval isselected to be six months, then four forecasts are generated.

With the time interval thus selected, the method further comprisesdetermining a locality adjustment (L_(p)). If the value of the item doesnot vary by geographic region (the value of the item is the same in theindustry across all localities at the initial time period), then nolocality adjustment needs to be made. Otherwise, the baseline value ofthe item at the initial time point t₀ may be adjusted by computing aratio between the average cost of items in the industry in a particularlocality at a certain time point t_(n) and the local cost of items inthe industry across all localities at the same time point t_(n). As anexample, consumer price index can be utilized to determine the costinformation on items in various industries relative to localities.

The exemplary method further comprises collecting or estimatingincremental values of modifications (M_(i,n)) to the base value of theitem of interest. These modifications can be of various types. Oneexample type can be modifications that are both observable at aparticular time t_(n) and are expected to retain some value in futuretime period(s) after the particular time t_(n). Another example type canbe modifications that are not observable and/or not expected to retainvalue after the particular time t_(n).

With the collected or estimated incremental values of modifications(M_(i,n)), the method may further comprise adjusting the base value ofthe item to account for the modifications and locality adjustments todetermine a locality-adjusted value of a modified item (BV_(i,n)) at theparticular time t_(n).

The method may further comprise constructing competitive sets of similarand substitute items in the same industry. This construction may involvepartitioning all items in the industry into distinct clusters based on ameasure of similarity between all pairs of items in the industry. A fullexplanation of an example competitive set approach is provided in U.S.patent application Ser. No. 13/173,332, filed Jun. 30, 2011, entitled“SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR PREDICTING ITEMPREFERENCE USING REVENUE-WEIGHTED COLLABORATIVE FILTER,” which is fullyincorporated herein by reference. Other competitive set approaches mayalso be possible.

To account for macroeconomic factor(s), the method may further comprisecollecting macroeconomic data, F_(.,n|n-h), and either forecastingfuture levels or incorporating existing forecasts from other sources todetermine {circumflex over (F)}_(.,n|n-h). Here, “F.” implies that themacroeconomic factors are taken over all industries and not specific toany particular industry p. “{circumflex over (F)}.” indicates that it isan estimated value.

To account for microeconomic factor(s), the method may further comprisecollecting microeconomic data, G_(p,n|n-h), for the industry p in whichthe item i being evaluated is classified and either forecasting futurelevels or incorporating existing forecasts from other sources todetermine Ĝ_(p,n|n-h). Here, each microeconomic factor is specific tothe industry p. “Ĝ” indicates that it is an estimated value.

To account for the depreciation (D_(i,n|0)) that represents the naturalchange in value that occurs as the item i is used over time, the methodmay further comprise using standard accounting definitions as a basisfor expressing the relationship between depreciation and time. Forexample, the natural change in value can represent a linear (straightline) depreciation. If there are no guidelines on buildingindustry-specific depreciation factors or no depreciation informationcan be obtained or provided by another source, the depreciation factorfrom another item j in the same competitive set may be used as asubstitute.

To compute a residual value for the item i ({circumflex over(V)}′_(i,n)) at the particular time t_(n) in one embodiment, the methodmay comprise solving the equation below using values constructed fromthe above-described steps.{circumflex over (V)}′ _(i,n) =BV _(i,n) ×D _(i,n|0)+({circumflex over(F)} _(.,n|n-h) +Ĝ _(p,n|n-h)) where h=1, . . . , H

Although the initial value of the item i, V_(i,0), is known and remainsunchanged, the forecasted residual value needn't also remain fixed overtime. As new information becomes available, it is possible to employthat additional information to update the forecasted value. This newinformation may be reflected in the variable types listed above.

In one embodiment, for quality assurance (QA), the forecasted residualvalues thus determined may be compared with a set of reference valuesand adjusted by an adjustment value, C_(iU,n|n*), that will minimize theweighted average error relative to the position implied by the referencepoints.

As an example, the approach for adjusting {circumflex over (V)}′_(i,n)for QA purposes may include the following steps:

-   -   Gather residual values from other goods in the competitive set,        including, for example:        -   The average residual value at t*_(n) for the entire            competitive set U_(k).        -   The baseline value BV_(j,0) at t₀ for the good j in the same            competitive set that is most similar to good i.        -   The residual value of the good k that is a previous version            of good i (not a modification of good i, but the one that            was replaced in production by good i), if it exists.    -   Compute the adjustment value, C_(iU,n|n*), that will minimizes        the weighted average error relative to the position implied by        the reference points as follows:        C _(iU,n|n*)=Δ[(αV _(U,n) +βBV _(j,0) +ΓV _(k,n))−V _(i,n)],        where α, β, Γ are assigned weights, α+β+Γ=1 and α, β, Γ>0, Δ is        a weight depending on whether good i at t_(n) is completely new        in the market (Δ=1) or established (Δ<1).

Adjusting {circumflex over (V)}′_(i,n) by C_(iU,n|n*) produces the finalforecasted residual value for the item i:{circumflex over (V)} _(i,n) =BV _(i,n) ×D _(j,n|0)+({circumflex over(F)} _(n|n-h) +Ĝ _(k,n|n-h))+C _(iU,n|n*) h=1, . . . , H

Those skilled in the art will appreciate that embodiments disclosedherein can apply to all types of items that are not immediately consumedand that retain some non-negative value over time. The variable typesdisclosed herein and are intended to encompass the various componentsrequired to value an item in any industry p and the microeconomic(G_(p)), Locality (L_(p)), and competitive sets (C_(iU)) components willbe specific to that industry class pertaining to the item i that isbeing valued as long as all other items or members, j=1, . . . , J, ofthe competitive set U are in the same industry, p, as item i.

Embodiments disclosed herein can provide many advantages. For example,knowledge of the future residual values can be used to:

-   a) Set leasing rates of an item which reflect the expected change in    valuation of the item between the beginning and ends of a fixed    lease period—a useful metric that can be used in the rental    industry.-   b) Determine the amount at which an item can be resold at any time    period—a useful metric that can be used in investment decisions such    as real estate.-   c) Provide information supporting the strategic planning decisions    made of the manufacturer of good i.-   d) Determine if the change in value will be constant over time    intervals of the same length.

Example Implementation in Automotive Industry

The following describes an exemplary implementation in which theapproach described above is adapted to be used to estimate the value ofautomobiles over time and thereby allows the resale values that could beexpected at future time points to be determined. Residual values thusestimated can provide guidelines for pricing fixed-term vehicle leaseswhich captures the expected change in value that will result in the timeinterval between the leased vehicle's acquisition at time t₀ and itsdisposition at time t_(d). This example implementation not only canprovide the estimated residual value at disposition, V_(i,d), but canalso forecast values at equally-spaced fixed points between t₀ andt_(d), thereby allowing construction of a residual curve that capturesthe relationship between vehicle value and time. Over time and as newinformation becomes available, this example implementation allows acompany to update their forecasts to reflect changing values ofexogenous macroeconomic and industry-specific microeconomic variablesand vehicle-specific, endogenous variables (depreciation, competitivesets, and modifications). In this example implementation, the guidelinesfor production of the residual values include:

-   -   The residual values must be realistic and adjusted over the        vehicle's lifecycle to reflect the market, incentives and fleet        purchases.    -   To enable vehicle manufacturers to market their vehicles,        residual values must create clear, consistent messages across        all vehicles being valued.    -   To remain relevant and timely, the residual values must reflect        product enhancements, packaging/content adjustments, etc.    -   To provide utility to each manufacturer's ecosystem, the        residual values must encompass all phases of the automotive        sales cycle, including dealer engagement, manufacturer support,        cooperation on pricing, and off lease supply management.

In this example, the methodology described above is adapted to estimateresidual values of cars and light trucks in the United States andCanada. Estimates are updated every two months to reflect new observeddata, market conditions, and macroeconomic estimates. In this example,the latest Model Year (MY) Toyota Camry LE with automatic transmission(AT)—which sells at popular equipped Manufacturer Suggested Retail Price(MSRP) of $23,800 in California—will be used. This particular model hassome historical used market value and residual value data available toestimate the future value of the vehicle. Furthermore, exogenousmacroeconomic and microeconomic data as well as endogenous factors(depreciation rate and competitive knowledge) are readily available toconstruct the current residual value curve for this vehicle at any term(e.g., 12-month, 24-month, 36-month, . . . , 60-month and any term inbetween).

Step 1. Determine a baseline, unmodified value for the durable good,V′_(i,0), e.g., through direct observation of the current market values.The 2010MY Camry LE AT baseline value for t₀ (set to June 2012, as anexample) is V′_(i,0)=$13,640 and is based on observed current marketvalue (CMV) derived from auction data. Roughly 990 auction records wereavailable in June 2012 for the 2010MY Camry LE AT to create the CMV of$13,640 by applying statistical filters and other measures to cleansethe data. For the purpose of illustration and not of limitation, auctionrecords may include such information as: Sale Date; National AutomobileDealers Association (NADA) Vehicle Identification Code; Make; Sub-make;Model Year; Series; Body Style; Diesel 4WD Identifier; NADA Region code;Sale Price; Mileage; Sale Type; Vehicle Identification Number (VIN);Vehicle Identifier (VID); etc.

Step 2. At time t_(n)=0, determine a reference period, t_(n*), at whichadjustments will be made to align values with other durable goods in thecompetitive set based on the following industry-level frequencies thatconstrain the choice of t_(n*):

-   -   auction data is updated weekly yet also aggregated to monthly        numbers, while microeconomic and macroeconomic factors are        updated monthly;    -   forecasted terms go up to 72-month, t_(max) is greater than        72-month;    -   most common terms are 12, 24, 36, 48, and 60-month terms, mostly        36-month is used.        Because a 36-month alignment is commonly used in the automotive        industry, a value of t_(n*)=36 months is used in this example        for the reference period relative to the baseline.

Step 3. At time t_(n)=0, determine the constant width of time intervalsΔ_((p.q)) at which forecasts will be generated. In this case, theselection of Δ_((p.q)) is determined by considering the followingconstraints;

-   -   It must be chosen such that (t_(n*)−t₀)/Δ_((p.q)) is a positive        integer where t_(n*)−t₀=36 months.    -   It must be greater than or equal to φ*=min_(r)(φ_(r))=weekly        since that is the frequency at which the macroeconomic data is        updated.        Given those constraints and a choice of t_(n*)=36, the        Δ_((p.q))=2 months is used.    -   36-month term/2 month=18>0.    -   Interval is greater than φ* (weekly data).

Step 4. Determine a locality adjustment, L_(p). If the base value of thegoods in industry p to which durable good i is assigned varies bygeographic region, then compute

$L_{p,n} = \frac{L_{p,n}^{\prime}(z)}{L_{p,n}^{\prime}(Z)}$where L′_(p,n)(z) is the average cost of durable goods in industry p inlocality z at time t_(n), and L′_(p,n)(Z) is the local cost of durablegoods in industry p across all localities (z ∈ Z) at time t_(n). In thisexample, the residual value of the 2010MY Toyota Camry LE AT is beingestablished for California, located in the z=“US West” region of theU.S. and where

${L_{west} = \frac{1.1}{1}},$local adjustment for U.S. Western region is 110% of the average for allregions in the U.S.

Step 5. Collect or estimate incremental values of modifications,M_(i,n), to the base configuration of the durable good. In this example,the vehicle has navigation added as popularly equipped which retains ameasurable and tangible value of $450 at 36-month. Thus,M_(Camry,36-month)=$450 for all regions; andM_(camry,36-month)=($450×1.1) for U.S. Western region.

Step 6. Determine the locality-adjusted value of the modified good i attime t_(n) by adjusting the base configuration's value to account formodifications and locality adjustments.BV _(i,n)=(V′ _(i,0) +M _(i,n))×(τ_(i,n) ×L_(p,n))=($13,640+$450)×1.1=$15,499

Step 7. Construct competitive sets, C_(iW,n), of similar and substitutedurable goods in the same industry, p. This involves determining factorsto compare to for each competitor and establish a matrix such as pricing(MSRP), engine and performance (horse power, mile per gallon, torque,displacement, etc.), exterior (curb weight, wheelbase, length, width,height, wheels size, etc.), interior (dimensions, features, airconditioning, entertainment system, seats, etc.) and safety.

Based on the factors above and the matrix analysis, for example, the2010 Honda Accord LX AT has the most similarities to the 2010 Camry LEAT, followed by 2010 Nissan Altima 2.5.

Step 8. Collect macroeconomic data, F_(.,n|n-h), and either forecastfuture levels or incorporate existing forecasts from other sources todetermine {circumflex over (F)}_(.,n|n-h).

In this case, housing prices, real wage growth, and gas prices arecollected at t₀ and forecasted for t_(n). For example, if housing priceindex is equal to 190 index points, real wage is 59 points, and averagegas prices are $3.56 per gallon in t₀, the forecasts are 202 points forhousing, 63 points for real wage, and $3.90 for gas price int_(36-month). The various factors have coefficients which determinedbased on correlation to auction data and thus the impact on theforecasted values can be applied by using the coefficients (seeequations described above). Hence, for example, based on the change inhousing price index from currently 190 to 202 in 36-month, the impact on36-month residual values is an incremental $200, from gas prices $170,and from wages $630. In this case, the total adjustment formacroeconomic variables is $1,000.

Step 9. Collect microeconomic data, G_(p,n|n-h), for the industry inwhich the good being evaluated is classified and forecast future levelsor incorporate existing forecasts from other sources to determineĜ_(p,n|n-h).

Microeconomic data, such as brand value, incentive spending, and rentalfleet penetration, can be generated for t₀ and forecasted fort_(36-month). For example, current incentive spending for the Camry is$2,900, yet the forecast is expected to be at $2,750. Thus, applying theequations described above and based on the change in incentive spendingfrom today to 36-month, the impact is an incremental $140, from rentalfleet penetration −$130, brand value $50, used supply $75, lifecycle−$85 and market volatility $1,300. In this case, the total adjustmentfor microeconomic variables is $1,250.

Step 10. Determine the natural depreciation values, D_(i,n|0), for thedurable good over time. Based on historical data, annual or 3-yeardepreciation can be constructed. In this example, the Camry LE AT isexpected to depreciate 14% annually.

Step 11. With all the pieces assembled, forecasting the residual valuefor time t_(n*)=36 month for the 2010 Toyota Camry LE AT (withnavigation, in California) can be done by substituting the valuesconstructed in earlier steps into the equation:

$\begin{matrix}{{\hat{V}}_{i,n}^{\prime} = {{{BV}_{i,n} \times D_{i,{n❘0}}} + ( {{\hat{F}}_{.{,{n❘{n - h}}}} + {\hat{G}}_{p,{n❘{n - h}}}} )}} \\{= {{( {( {{{\$ 13},640} + {\$ 450}} ) \times 1.1} ) \times ( {1 - 0.14} )^{3}} + ( {{{\$ 1},000} - {{\$ 1},250}} )}} \\{= {{{\$ 9},850} - {\$ 250}}} \\{= {\$ 9600}}\end{matrix}$

Step 12. Perform quality assurance (QA). In this example, this involvescomputing the adjustment value, C_(iU,n|n*), that will minimizes theweighted average error relative to the position implied by the referencepoints. The adjustment value in the case of the Camry LE AT is small(Δ<1), since it is an established good and plenty of history isavailable. The average residual value of entire competitive set is$9,200 and the following factors are taken into account:

-   -   i. Baseline of the closest competitor is $9,500    -   ii. 2009MY Toyota Camry LE is $10,000        Applying the equation described above, the adjustment value        C_(iU,n|n*) is then equal to        0.25×((0.33×9,200+0.33×$9,500+0.33×$10,000)−$9,600)=−$32

Step 13. Adjust {circumflex over (V)}′_(i,n) by C_(iU,n|n*) to determinethe final forecasted value:

In this example, the final forecast for the 2010MY Toyota Camry LE ATfor the Western region for time t_(n*)=36 month is $9,600−$32=$9,568.

These, and other, aspects of the disclosure and various features andadvantageous details thereof are explained more fully with reference tothe exemplary, and therefore non-limiting, embodiments illustrated anddetailed in this disclosure. It should be understood, however, that thedetailed description and the specific examples, while indicating thepreferred embodiments, are given by way of illustration only and not byway of limitation. Descriptions of known programming techniques,computer software, hardware, operating platforms and protocols may beomitted so as not to unnecessarily obscure the disclosure in detail.Various substitutions, modifications, additions and/or rearrangementswithin the spirit and/or scope of the underlying inventive concept willbecome apparent to those skilled in the art from this disclosure.

Embodiments discussed herein can be implemented in a computercommunicatively connected to a network (for example, the Internet),another computer, or in a standalone computer. As is known to thoseskilled in the art, a suitable computer can include a central processingunit (“CPU”), at least one read-only memory (“ROM”), at least one randomaccess memory (“RAM”), at least one hard drive (“HD”), and one or moreinput/output (“I/O”) device(s). The I/O devices can include a keyboard,monitor, printer, electronic pointing device (for example, mouse,trackball, stylus, touch pad, etc.), or the like. In embodiments of theinvention, the computer has access to at least one database over anetwork connection.

ROM, RAM, and HD are computer memories for storing computer-executableinstructions executable by the CPU or capable of being compiled orinterpreted to be executable by the CPU. Suitable computer-executableinstructions may reside on a computer readable medium (e.g., ROM, RAM,and/or HD), hardware circuitry or the like, or any combination thereof.Within this disclosure, the term “computer readable medium” or is notlimited to ROM, RAM, and HD and can include any type of data storagemedium that can be read by a processor. Examples of computer-readablestorage media can include, but are not limited to, volatile andnon-volatile computer memories and storage devices such as random accessmemories, read-only memories, hard drives, data cartridges, directaccess storage device arrays, magnetic tapes, floppy diskettes, flashmemory drives, optical data storage devices, compact-disc read-onlymemories, and other appropriate computer memories and data storagedevices. Thus, a computer-readable medium may refer to a data cartridge,a data backup magnetic tape, a floppy diskette, a flash memory drive, anoptical data storage drive, a CD-ROM, ROM, RAM, HD, or the like.

The processes described herein may be implemented in suitablecomputer-executable instructions that may reside on a computer readablemedium (for example, a disk, CD-ROM, a memory, etc.). Alternatively, thecomputer-executable instructions may be stored as software codecomponents on a direct access storage device array, magnetic tape,floppy diskette, optical storage device, or other appropriatecomputer-readable medium or storage device.

Any suitable programming language can be used to implement the routines,methods or programs of embodiments of the invention described herein,including C, C++, Java, JavaScript, HTML, or any other programming orscripting code, etc. Other software/hardware/network architectures maybe used. For example, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network. Communications between computersimplementing embodiments can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols.

Different programming techniques can be employed such as procedural orobject oriented. Any particular routine can execute on a single computerprocessing device or multiple computer processing devices, a singlecomputer processor or multiple computer processors. Data may be storedin a single storage medium or distributed through multiple storagemediums, and may reside in a single database or multiple databases (orother data storage techniques). Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different embodiments. In some embodiments, to the extentmultiple steps are shown as sequential in this specification, somecombination of such steps in alternative embodiments may be performed atthe same time. The sequence of operations described herein can beinterrupted, suspended, or otherwise controlled by another process, suchas an operating system, kernel, etc. The routines can operate in anoperating system environment or as stand-alone routines. Functions,routines, methods, steps and operations described herein can beperformed in hardware, software embodied on hardware, firmware or anycombination thereof.

Embodiments described herein can be implemented in the form of controllogic in hardware or a combination of software and hardware. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

It is also within the spirit and scope of the invention to implement insoftware programming or code an of the steps, operations, methods,routines or portions thereof described herein, where such softwareprogramming or code can be stored in a computer-readable medium and canbe operated on by a processor to permit a computer to perform any of thesteps, operations, methods, routines or portions thereof describedherein. The invention may be implemented by using software programmingor code in one or more general purpose digital computers, by usingapplication specific integrated circuits, programmable logic devices,field programmable gate arrays, optical, chemical, biological, quantumor nanoengineered systems, components and mechanisms may be used. Ingeneral, the functions of the invention can be achieved by any means asis known in the art. For example, distributed, or networked systems,components and circuits can be used. In another example, communicationor transfer (or otherwise moving from one place to another) of data maybe wired, wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system ordevice. The computer readable medium can be, by way of example only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall generally be machine readable and include software programming orcode that can be human readable (e.g., source code) or machine readable(e.g., object code).

Examples of non-transitory computer-readable media can include randomaccess memories, read-only memories, hard drives, data cartridges,magnetic tapes, floppy diskettes, flash memory drives, optical datastorage devices, compact-disc read-only memories, and other appropriatecomputer memories and data storage devices. In an illustrativeembodiment, some or all of the software components may reside on asingle server computer or on any combination of separate servercomputers. As one skilled in the art can appreciate, a computer programproduct implementing an embodiment disclosed herein may comprise one ormore non-transitory computer readable media storing computerinstructions translatable by one or more processors in a computingenvironment.

A “computer” or “processor” may include any hardware system, mechanismor component that processes data, signals or other information. Acomputer or processor can include a system with a general-purposecentral processing unit, multiple processing units, dedicated circuitryfor achieving functionality, or other systems. Processing need not belimited to a geographic location, or have temporal limitations. Forexample, a computer or processor can perform its functions in“real-time,” “offline,” in a “batch mode,” etc. Portions of processingcan be performed at different times and at different locations, bydifferent (or the same) processing systems.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, product, article, orapparatus.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. For example, a condition A or B issatisfied by any one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein,including the accompanying appendices, a term preceded by “a” or “an”(and “the” when antecedent basis is “a” or “an”) includes both singularand plural of such term, unless clearly indicated otherwise (i.e., thatthe reference “a” or “an” clearly indicates only the singular or onlythe plural). Also, as used in the description herein and in theaccompanying appendices, the meaning of “in” includes “in” and “on”unless the context clearly dictates otherwise.

Although the foregoing specification describes specific embodiments,numerous changes in the details of the embodiments disclosed herein andadditional embodiments will be apparent to, and may be made by, personsof ordinary skill in the art having reference to this disclosure. Inthis context, the specification and figures are to be regarded in anillustrative rather than a restrictive sense, and all such modificationsare intended to be included within the scope of this disclosure.Accordingly, the scope of this disclosure should be determined by thefollowing claims and their legal equivalents.

What is claimed is:
 1. A method for production of automotive residualvalues, the method comprising: determining a baseline value for avehicle with a base configuration, the determining performed by acomputer at a base time based on a market value derived from auctiondata; determining, by the computer at the base time, a reference timeperiod for value alignment with vehicles in a competitive set, the valuealignment constrained by automotive industry-level frequencies, theautomotive industry-level frequencies including a frequency for whichmacroeconomic data is updated; determining, by the computer at the basetime given a set of constraints, a constant width of time intervals, theset of constraints include a length of time constraint relating to thereference time period, and a frequency constraint relating to thefrequency for which the macroeconomic data is updated; determining, bythe computer, a locality adjustment based on an average cost of vehiclesin the automotive industry in a locality at a reference time and a localcost of vehicles in the automotive industry across a plurality oflocalities at the reference time; obtaining, by the computer, anyincremental values of modification to the base configuration of thevehicle; producing, by the computer, a locality-adjusted value of thevehicle at the reference time, the producing comprising adjusting thebaseline value associated with the base configuration to account for anymodifications to the base configuration of the vehicle and to accountfor the locality adjustment; determining, by the computer, an adjustmentfor macroeconomic variables at the reference time based at least in parton the macroeconomic data collected at the base time; determining, bythe computer, an adjustment for microeconomic variables at the referencetime based at least in part on the microeconomic data collected at thebase time; generating, by the computer, a residual value of the vehicleat the reference time with the adjustment for the macroeconomicvariables at the reference time and the adjustment for the microeconomicvariables at the reference time; and communicating over a network theresidual value of the vehicle at the reference time to a graphical userinterface on a client device.
 2. The method according to claim 1,further comprising: constructing competitive sets of similar andsubstitute vehicles in the automotive industry, the constructingcomprising determining factors to compare to for each competitor,establishing a matrix of vehicle features, and performing a matrixanalysis on the matrix.
 3. The method according to claim 2, furthercomprising: determining a quality assurance adjustment based at least inpart on an average residual value of a competitive set of thecompetitive sets, the residual value of the vehicle at the referencetime, and a baseline of a closest competitor from the competitive sets;and applying the quality assurance adjustment to the residual value ofthe vehicle at the reference time.
 4. The method according to claim 1,further comprising: determining natural depreciation values for thevehicle over time based on historical data, wherein the residual valueof the vehicle at the reference time accounts for the naturaldepreciation values.
 5. The method according to claim 1, wherein theauction data comprises auction records from data sources external to thecomputer.
 6. The method according to claim 1, wherein the computercollects in a data store data of a plurality of data types from variousdata sources, the plurality of data types including the modifications,the locality, the macroeconomic data, and the microeconomic data.
 7. Themethod according to claim 1, wherein the microeconomic data is specificto the automotive industry and wherein the macroeconomic data is notspecific to the vehicle or to the automotive industry.
 8. A system forproduction of automotive residual values, the system comprising: atleast one processor; at least one non-transitory computer-readablemedium; and stored instructions translatable by the at least oneprocessor to perform: determining a baseline value for a vehicle with abase configuration, the determining performed at a base time based on amarket value derived from auction data; determining, at the base time, areference time period for value alignment with vehicles in a competitiveset, the value alignment constrained by automotive industry-levelfrequencies, the automotive industry-level frequencies including afrequency for which macroeconomic data is updated; determining, at thebase time given a set of constraints, a constant width of timeintervals, the set of constraints include a length of time constraintrelating to the reference time period, and a frequency constraintrelating to the frequency for which the macroeconomic data is updated;determining a locality adjustment based on an average cost of vehiclesin the automotive industry in a locality at a reference time and a localcost of vehicles in the automotive industry across a plurality oflocalities at the reference time; obtaining any incremental values ofmodification to the base configuration of the vehicle; producing alocality-adjusted value of the vehicle at the reference time, theproducing comprising adjusting the baseline value associated with thebase configuration to account for any modifications to the baseconfiguration of the vehicle and to account for the locality adjustment;determining an adjustment for macroeconomic variables at the referencetime based at least in part on the macroeconomic data collected at thebase time; determining an adjustment for microeconomic variables at thereference time based at least in part on the microeconomic datacollected at the base time; generating a residual value of the vehicleat the reference time with the adjustment for the macroeconomicvariables at the reference time and the adjustment for the microeconomicvariables at the reference time; and communicating over a network theresidual value of the vehicle at the reference time to a graphical userinterface on a client device.
 9. The system of claim 8, wherein theinstructions are further translatable by the at least one processor toperform: constructing competitive sets of similar and substitutevehicles in the automotive industry, the constructing comprisingdetermining factors to compare to for each competitor, establishing amatrix of vehicle features, and performing a matrix analysis on thematrix.
 10. The system of claim 9, wherein the instructions are furthertranslatable by the at least one processor to perform: determining aquality assurance adjustment based at least in part on an averageresidual value of a competitive set of the competitive sets, theresidual value of the vehicle at the reference time, and a baseline of aclosest competitor from the competitive sets; and applying the qualityassurance adjustment to the residual value of the vehicle at thereference time.
 11. The system of claim 8, wherein the instructions arefurther translatable by the at least one processor to perform:determining natural depreciation values for the vehicle over time basedon historical data, wherein the residual value of the vehicle at thereference time accounts for the natural depreciation values.
 12. Thesystem of claim 8, wherein the auction data comprises auction recordsfrom data sources external to the computer.
 13. The system of claim 8,further comprising: a data store storing data of a plurality of datatypes from various data sources, the plurality of data types includingthe modifications, the locality, the macroeconomic data, and themicroeconomic data.
 14. The system of claim 8, wherein the microeconomicdata is specific to the automotive industry and wherein themacroeconomic data is not specific to the vehicle or to the automotiveindustry.
 15. A computer program product for production of automotiveresidual values, the computer program product comprising at least onenon-transitory computer-readable medium storing instructionstranslatable by at least one processor to perform: determining abaseline value for a vehicle with a base configuration, the determiningperformed at a base time based on a market value derived from auctiondata; determining, at the base time, a reference time period for valuealignment with vehicles in a competitive set, the value alignmentconstrained by automotive industry-level frequencies, the automotiveindustry-level frequencies including a frequency for which macroeconomicdata is updated; determining, at the base time given a set ofconstraints, a constant width of time intervals, the set of constraintsinclude a length of time constraint relating to the reference timeperiod, and a frequency constraint relating to the frequency for whichthe macroeconomic data is updated; determining a locality adjustmentbased on an average cost of vehicles in the automotive industry in alocality at a reference time and a local cost of vehicles in theautomotive industry across a plurality of localities at the referencetime; obtaining any incremental values of modification to the baseconfiguration of the vehicle; producing a locality-adjusted value of thevehicle at the reference time, the producing comprising adjusting thebaseline value associated with the base configuration to account for anymodifications to the base configuration of the vehicle and to accountfor the locality adjustment; determining an adjustment for macroeconomicvariables at the reference time based at least in part on themacroeconomic data collected at the base time; determining an adjustmentfor microeconomic variables at the reference time based at least in parton the microeconomic data collected at the base time; generating aresidual value of the vehicle at the reference time with the adjustmentfor the macroeconomic variables at the reference time and the adjustmentfor the microeconomic variables at the reference time; and communicatingover a network the residual value of the vehicle at the reference timeto a graphical user interface on a client device.
 16. The computerprogram product of claim 15, wherein the instructions are furthertranslatable by the at least one processor to perform: constructingcompetitive sets of similar and substitute vehicles in the automotiveindustry, the constructing comprising determining factors to compare tofor each competitor, establishing a matrix of vehicle features, andperforming a matrix analysis on the matrix.
 17. The computer programproduct of claim 16, wherein the instructions are further translatableby the at least one processor to perform: determining a qualityassurance adjustment based at least in part on an average residual valueof a competitive set of the competitive sets, the residual value of thevehicle at the reference time, and a baseline of a closest competitorfrom the competitive sets; and applying the quality assurance adjustmentto the residual value of the vehicle at the reference time.
 18. Thecomputer program product of claim 15, wherein the instructions arefurther translatable by the at least one processor to perform:determining natural depreciation values for the vehicle over time basedon historical data, wherein the residual value of the vehicle at thereference time accounts for the natural depreciation values.
 19. Thecomputer program product of claim 15, wherein the auction data comprisesauction records from data sources external to the computer.
 20. Thecomputer program product of claim 15, wherein the microeconomic data isspecific to the automotive industry and wherein the macroeconomic datais not specific to the vehicle or to the automotive industry.